The visual salient mechanism can be used to extract the salient areas of significant object which may have influence on traffic safety in complex road. For traditional object detection method in complex road scenes, the related algorithm is very complex, then the data processing capacity of the computer will be very large and the processing efficiency will be very low. Nowadays, the researchers are inspired by the visual attention mechanism in the human visual system, hoping that machine vision would first screen the raw input data like the human visual system. Therefore, the visual salient model of machine vision is proposed to reduce computation and improve its efficiency.
Visual saliency is a concept of neurophysiology and psychology. Many neurophysiologists and psychologists have put forward a lot of models to explain the saliency mechanism of the human visual system, such as the peripheral central nervous system, which simulates the ability of efficient data screening in human visual attention mechanism. Inspired by this, researchers in various countries have launched extensive and in-depth research on how to screen data in machine vision like human visual saliency mechanism.
Chinese invention (CN102999926A) disclosed is an image visual saliency calculation method based on low-level feature fusion which fuse underlying features such as colors, textures, etc. to obtain a significant area. This invention just based on low-level features while ignore high-level features so that its saliency extraction is inaccurate. Chinese invention (CN103020993A) disclosed a dual-channel color contrast fusion visual saliency detection method which use the superpixel method to calculate the value of color saliency to obtain a saliency image based on color features. This invention only processes in the time domain, and is based only on color characteristics, ignoring the significance in the frequency domain, so the extraction of the significance area is not complete. The above method has its own deficiencies in the process of extracting the salient regions, and deals with a natural and natural image without considering that the night vision infrared image corresponding to the natural image can be processed. Therefore, in the night road scenario, their detection rate is low, and are easily disturbed by the light in the background environment, and the outline of the prominent target is not clear enough.
By processing the corresponding night vision infrared images of the original image, the present invention tend to avoid the influence of the light in the non-visible background of the original image at night, so as to improve the accuracy of the salient target extraction in the night road scene.